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Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications

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Abstract

Ever since its foundational years, synthetic biology has been focused on the implementation of biological computing structures. In the beginning, engineered biological computation has mainly been based on uncoupled monoclonal cellular populations. Implementations of such computing structures were mostly inspired by digital electronic circuits and revealed many constraints that limited the advance of the field to relatively simple information processing structures. The focus has recently shifted towards the implementation of biological computing structures within coupled intercellular circuits composed of engineered cellular modules. These circuits have, however, advanced only to a certain point, namely to consist of a few engineered bacterial strains, which perform the computation. It is now time to make a transition from modules and relatively simple systems of biological processing structures to networks composing different strains each presenting a designated computing structure. In such networks, each strain is analogous to a logic chip on a breadboard circuit and is connected to other strains by means of intercellular communication mechanisms rather than copper wires. This analogy can be driven further to use a set of engineered biological modules to construct a complex computing system, such as a multicellular biological processor. We review the state of the art of distributed cellular computation, communication mechanisms, and computational analysis and design approaches for distributed biological computing. We demonstrate the potential next step in engineered biological computation by a proposal of a design of a multicellular biological processor. We demonstrate an analysis of the proposed computing network using in silico simulation and optimisation approaches. Finally, we discuss the potential applications of the reviewed distributed cellular computing structures to the field of neural computing.

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Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Code availability

The code that can be used to reproduce the results reported in the article is available at https://github.com/roman-komac/distributed-bio-computing.

References

  1. Grozinger L, Amos M, Gorochowski TE, Carbonell P, Oyarzún DA, Stoof R, Fellermann H, Zuliani P, Tas H, Goñi-Moreno A (2019) Pathways to cellular supremacy in biocomputing. Nat Commun 10(1):1–11

    Article  Google Scholar 

  2. El Karoui M, Hoyos-Flight M, Fletcher L (2019) Future trends in synthetic biology—a report. Front Bioeng Biotechnol 7:175

    Article  Google Scholar 

  3. Jain K (2013) Synthetic biology and personalized medicine. Med Princ Pract 22(3):209–219

    Article  Google Scholar 

  4. Chubukov V, Mukhopadhyay A, Petzold CJ, Keasling JD, Martín HG (2016) Synthetic and systems biology for microbial production of commodity chemicals. npj Syst Biol Appl 2(1):1–11

    Article  Google Scholar 

  5. Breitling R, Takano E (2015) Synthetic biology advances for pharmaceutical production. Curr Opin Biotechnol 35:46–51

    Article  Google Scholar 

  6. Jagadevan S, Banerjee A, Banerjee C, Guria C, Tiwari R, Baweja M, Shukla P (2018) Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production. Biotechnol Biofuels 11(1):185

    Article  Google Scholar 

  7. Narnoliya LK, Jadaun JS, Singh SP (2018) Management of agro-industrial wastes with the aid of synthetic biology. In: Varjani S, Parameswaran B, Kumar S, Khare S (eds) Biosynthetic technology and environmental challenges. Springer, Berlin, pp 11–28

    Chapter  Google Scholar 

  8. Toda S, Brunger JM, Lim WA (2019) Synthetic development: learning to program multicellular self-organization. Curr Opin Syst Biol 14:41–49

    Article  Google Scholar 

  9. Hicks M, Bachmann TT, Wang B (2020) Synthetic biology enables programmable cell-based biosensors. ChemPhysChem 21(2):132–144

    Article  Google Scholar 

  10. Weiss R, Homsy GE, Knight TF (2002) Toward in vivo digital circuits. In: Landweber LF, Winfree E (eds) Evolution as computation. Springer, Berlin, pp 275–295

    Chapter  Google Scholar 

  11. Wang B, Kitney RI, Joly N, Buck M (2011) Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology. Nat Commun 2(1):1–9

    Article  Google Scholar 

  12. Burrill D, Silver P (2010) Making cellular memories. Cell 140(1):13–18

    Article  Google Scholar 

  13. Burrill D, Inniss M, Boyle P, Silver P (2012) Synthetic memory circuits for tracking human cell fate. Genes Dev 26(13):1486–1497

    Article  Google Scholar 

  14. Inniss M, Silver P (2013) Building synthetic memory. Curr Biol 23(17):R812–R816

    Article  Google Scholar 

  15. Sonnen KF, Aulehla A (2014) Dynamic signal encoding—from cells to organisms. In: Gallouzi IE, Aulehla A, Woolner S (eds) Seminars in cell & developmental biology, vol 34. Elsevier, Amsterdam, pp 91–98

    Google Scholar 

  16. Chuang CH, Lin CL (2014) Synthesizing genetic sequential logic circuit with clock pulse generator. BMC Syst Biol 8(1):63

    Article  Google Scholar 

  17. Magdevska L, Pušnik Ž, Mraz M, Zimic N, Moškon M (2017) Computational design of synchronous sequential structures in biological systems. J Comput Sci 18:24–31

    Article  MathSciNet  Google Scholar 

  18. Kwok R (2010) Five hard truths for synthetic biology: can engineering approaches tame the complexity of living systems? Roberta Kwok explores five challenges for the field and how they might be resolved. Nature 463(7279):288–291

    Article  Google Scholar 

  19. Goñi-Moreno A, Amos M, de la Cruz F (2013) Multicellular computing using conjugation for wiring. PLoS One 8(6):e65986

    Article  Google Scholar 

  20. Macía J, Posas F, Solé RV (2012) Distributed computation: the new wave of synthetic biology devices. Trends Biotechnol 30(6):342–349

    Article  Google Scholar 

  21. Zhang C, Tsoi R, You L (2016) Addressing biological uncertainties in engineering gene circuits. Integr Biol 8(4):456–464

    Article  Google Scholar 

  22. Liu Q, Schumacher J, Wan X, Lou C, Wang B (2017) Orthogonality and burdens of heterologous AND gate gene circuits in E. coli. ACS Synth Biol 7(2):553–564

    Article  Google Scholar 

  23. Yeo NC, Chavez A, Lance-Byrne A, Chan Y, Menn D, Milanova D, Kuo CC, Guo X, Sharma S, Tung A et al (2018) An enhanced CRISPR repressor for targeted mammalian gene regulation. Nat Methods 15(8):611

    Article  Google Scholar 

  24. Kim J, Zhou Y, Carlson PD, Teichmann M, Chaudhary S, Simmel FC, Silver PA, Collins JJ, Lucks JB, Yin P et al (2019) De novo-designed translation-repressing riboregulators for multi-input cellular logic. Nat Chem Biol 15:1173–1182

    Article  Google Scholar 

  25. Gräwe A, Ranglack J, Weber W, Stein V (2020) Engineering artificial signalling functions with proteases. Curr Opin Biotechnol 63:1–7

    Article  Google Scholar 

  26. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330(6007):1099–1102

    Article  Google Scholar 

  27. Wu G, Yan Q, Jones JA, Tang YJ, Fong SS, Koffas MA (2016) Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol 34(8):652–664

    Article  Google Scholar 

  28. Nikolados EM, Weiße AY, Ceroni F, Oyarzún DA (2019) Growth defects and loss-of-function in synthetic gene circuits. ACS Synth Biol 8(6):1231–1240

    Article  Google Scholar 

  29. Tsoi R, Wu F, Zhang C, Bewick S, Karig D, You L (2018) Metabolic division of labor in microbial systems. Proc Natl Acad Sci USA 115(10):2526–2531

    Article  Google Scholar 

  30. Pai A, Tanouchi Y, Collins CH, You L (2009) Engineering multicellular systems by cell–cell communication. Curr Opin Biotechnol 20(4):461–470

    Article  Google Scholar 

  31. Kong W, Celik V, Liao C, Hua Q, Lu T (2014) Programming the group behaviors of bacterial communities with synthetic cellular communication. Bioresour Bioprocess 1(1):24

    Article  Google Scholar 

  32. Brenner K, You L, Arnold FH (2008) Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol 26(9):483–489

    Article  Google Scholar 

  33. Sadeghpour M, Veliz-Cuba A, Orosz G, Josić K, Bennett MR (2017) Bistability and oscillations in co-repressive synthetic microbial consortia. Quant Biol 5(1):55–66

    Article  Google Scholar 

  34. Urrios A, Gonzalez-Flo E, Canadell D, De Nadal E, Macía J, Posas F (2018) Plug-and-play multicellular circuits with time-dependent dynamic responses. ACS Synth Biol 7(4):1095–1104

    Article  Google Scholar 

  35. Tanouchi Y, Tu D, Kim J, You L (2008) Noise reduction by diffusional dissipation in a minimal quorum sensing motif. PLoS Comput Biol 4(8):e1000167

    Article  Google Scholar 

  36. Koseska A, Zaikin A, Kurths J, García-Ojalvo J (2009) Timing cellular decision making under noise via cell–cell communication. PLoS One 4(3):e4872

    Article  Google Scholar 

  37. Macía J, Manzoni R, Conde N, Urrios A, de Nadal E, Solé R, Posas F (2016) Implementation of complex biological logic circuits using spatially distributed multicellular consortia. PLoS Comput Biol 12(2):1–24

    Article  Google Scholar 

  38. Amos M, Goñi-Moreno A (2018) Cellular computing and synthetic biology. In: Stepney S, Rasmussen S, Amos M (eds) Computational matter, no. 2012 in natural computing series. Springer, Berlin, pp 93–110

  39. Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335–338

    Article  Google Scholar 

  40. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403:339–342

    Article  Google Scholar 

  41. Purnick PEM, Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol 10(6):410–22

    Article  Google Scholar 

  42. Amos M (2014) Population-based microbial computing: a third wave of synthetic biology? Int J Gen Syst 43(7):770–782

    Article  Google Scholar 

  43. Cameron DE, Bashor CJ, Collins JJ (2014) A brief history of synthetic biology. Nat Rev Microbiol 12:381–390

    Article  Google Scholar 

  44. Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11(5):367

    Article  Google Scholar 

  45. Müller S, Hofbauer J, Endler L, Flamm C, Widder S, Schuster P (2006) A generalized model of the repressilator. J Math Biol 53(6):905–937

    Article  MathSciNet  MATH  Google Scholar 

  46. Buse O, Pérez R, Kuznetsov A (2010) Dynamical properties of the repressilator model. Phys Rev E 81(6):066206

    Article  MathSciNet  Google Scholar 

  47. Strelkowa N, Barahona M (2010) Switchable genetic oscillator operating in quasi-stable mode. J R Soc Interface 7(48):1071–1082

    Article  Google Scholar 

  48. Purcell O, Savery NJ, Grierson CS, di Bernardo M (2010) A comparative analysis of synthetic genetic oscillators. J R Soc Interface 7(52):1503–1524

    Article  Google Scholar 

  49. Potvin-Trottier L, Lord ND, Vinnicombe G, Paulsson J (2016) Synchronous long-term oscillations in a synthetic gene circuit. Nature 538(7626):514–517

    Article  Google Scholar 

  50. Pett JP, Korenčič A, Wesener F, Kramer A, Herzel H (2016) Feedback loops of the mammalian circadian clock constitute repressilator. PLoS Comput Biol 12(12):e1005266

    Article  Google Scholar 

  51. Shopera T, Henson WR, Ng A, Lee YJ, Ng K, Moon TS (2015) Robust, tunable genetic memory from protein sequestration combined with positive feedback. Nucleic Acids Res 43(18):9086–9094

    Article  Google Scholar 

  52. Andrews LB, Nielsen AAK, Voigt CA (2018) Cellular checkpoint control using programmable sequential logic. Science 361(6408):eaap8987

    Article  Google Scholar 

  53. Atkinson S, Williams P (2009) Quorum sensing and social networking in the microbial world. J R Soc Interface 6(40):959–978

    Article  Google Scholar 

  54. Perez-Carrasco R, Barnes CP, Schaerli Y, Isalan M, Briscoe J, Page KM (2018) Combining a toggle switch and a repressilator within the AC–DC circuit generates distinct dynamical behaviors. Cell Syst 6(4):521–530

    Article  Google Scholar 

  55. Wang YH, Wei KY, Smolke CD (2013) Synthetic biology: advancing the design of diverse genetic systems. Annu Rev Chem Biomol Eng 4:69–102

    Article  Google Scholar 

  56. Singh V (2014) Recent advances and opportunities in synthetic logic gates engineering in living cells. Syst Synth Biol 8(4):271–282

    Article  Google Scholar 

  57. Chen Z, Kibler RD, Hunt A, Busch F, Pearl J, Jia M, VanAernum ZL, Wicky BI, Dods G, Liao H et al (2020) De novo design of protein logic gates. Science 368(6486):78–84

    Article  Google Scholar 

  58. Fink T, Lonzarić J, Praznik A, Plaper T, Merljak E, Leben K, Jerala N, Lebar T, Strmšek Ž, Lapenta F et al (2019) Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat Chem Biol 15(2):115

    Article  Google Scholar 

  59. Gao XJ, Chong LS, Kim MS, Elowitz MB (2018) Programmable protein circuits in living cells. Science 361(6408):1252–1258

    Article  Google Scholar 

  60. Gao C, Hou J, Xu P, Guo L, Chen X, Hu G, Ye C, Edwards H, Chen J, Chen W et al (2019) Programmable biomolecular switches for rewiring flux in Escherichia coli. Nat Commun 10(1):1–12

    Article  Google Scholar 

  61. McCarty NS, Ledesma-Amaro R (2019) Synthetic biology tools to engineer microbial communities for biotechnology. Trends Biotechnol 37(2):181–197

    Article  Google Scholar 

  62. Regot S, MacIa J, Conde N, Furukawa K, Kjellén J, Peeters T, Hohmann S, De Nadal E, Posas F, Solé R (2011) Distributed biological computation with multicellular engineered networks. Nature 469(7329):207–211

    Article  Google Scholar 

  63. Danino T, Mondragón-Palomino O, Tsimring L, Hasty J (2010) A synchronized quorum of genetic clocks. Nature 463(7279):326–330

    Article  Google Scholar 

  64. Verma SC, Miyashiro T (2013) Quorum sensing in the squid-Vibrio symbiosis. Int J Mol Sci 14(8):16386–16401

    Article  Google Scholar 

  65. Grant PK, Dalchau N, Brown JR, Federici F, Rudge TJ, Yordanov B, Patange O, Phillips A, Haseloff J (2016) Orthogonal intercellular signaling for programmed spatial behavior. Mol Syst Biol 12(1):849

    Article  Google Scholar 

  66. Marchand N, Collins CH (2013) Peptide-based communication system enables Escherichia coli to Bacillus megaterium interspecies signaling. Biotechnol Bioeng 110(11):3003–3012

    Article  Google Scholar 

  67. Scott SR, Hasty J (2016) Quorum sensing communication modules for microbial consortia. ACS Synth Biol 5(9):969–977

    Article  Google Scholar 

  68. Shannon CE (1948) A mathematical theory of communication. Bell Labs Tech J 27(379–423):623–656

    Article  MathSciNet  MATH  Google Scholar 

  69. Ortiz ME, Endy D (2012) Engineered cell–cell communication via DNA messaging. J Biol Eng 6:16

    Article  Google Scholar 

  70. Gutiérrez M, Ortiz Y, Carrión J (2020) A framework for implementing metaheuristic algorithms using intercellular communication. Arxiv pp 1–34

  71. Bonnet J, Yin P, Ortiz ME, Subsoontorn P, Endy D (2013) Amplifying genetic logic gates. Science 340(6132):599–603

    Article  Google Scholar 

  72. Chen Y, Kim JK, Hirning AJ, Josić K, Bennett MR (2015) Emergent genetic oscillations in a synthetic microbial consortium. Science 349(6251):986–989

    Article  Google Scholar 

  73. Urrios A, Macía J, Manzoni R, Conde N, Bonforti A, De Nadal E, Posas F, Solé R (2016) A synthetic multicellular memory device. ACS Synth Biol 5(8):862–873

    Article  Google Scholar 

  74. Basu S, Gerchman Y, Collins CH, Arnold FH, Weiss R (2005) A synthetic multicellular system for programmed pattern formation. Nature 434(7037):1130–1134

    Article  Google Scholar 

  75. Tabor JJ, Salis HM, Simpson ZB, Chevalier AA, Levskaya A, Marcotte EM, Voigt CA, Ellington AD (2009) A synthetic genetic edge detection program. Cell 137(7):1272–1281

    Article  Google Scholar 

  76. Brenner K, Karig DK, Weiss R, Arnold FH (2007) Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium. Proc Natl Acad Sci USA 104(44):17300–17304

    Article  Google Scholar 

  77. Garcia-Ojalvo J, Elowitz MB, Strogatz SH (2004) Modeling a synthetic multicellular clock: repressilators coupled by quorum sensing. Proc Natl Acad Sci USA 101(30):10955–10960

    Article  MathSciNet  MATH  Google Scholar 

  78. Tamsir A, Tabor JJ, Voigt CA (2011) Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature 469(7329):212–215

    Article  Google Scholar 

  79. Sardanyés J, Bonforti A, Conde N, Solé R, Macía J (2015) Computational implementation of a tunable multicellular memory circuit for engineered eukaryotic consortia. Front Physiol 6:281

    Article  Google Scholar 

  80. Macía J, Vidiella B, Solé RV (2017) Synthetic associative learning in engineered multicellular consortia. J R Soc Interface 14(129):20170158

    Article  Google Scholar 

  81. Gonzalez-Flo E, Alaball Pujol ME, Macía J (2020) Two-component biosensors: unveiling the mechanisms of predictable tunability. ACS Synth Biol 9(6):1328–1335

    Article  Google Scholar 

  82. de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103

    Article  MathSciNet  Google Scholar 

  83. Kaveh A, Koohestani K (2008) Graph products for configuration processing of space structures. Comput Struct 86(11–12):1219–1231

    Article  Google Scholar 

  84. Le Novere N (2015) Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet 16(3):146–158

    Article  Google Scholar 

  85. Widmer LA, Stelling J (2018) Bridging intracellular scales by mechanistic computational models. Curr Opin Biotechnol 52:17–24

    Article  Google Scholar 

  86. Kaern M, Blake WJ, Collins J (2003) The engineering of gene regulatory networks. Annu Rev Biomed Eng 5:179–206

    Article  Google Scholar 

  87. Nikolaev EV, Sontag ED (2016) Quorum-sensing synchronization of synthetic toggle switches: a design based on monotone dynamical systems theory. PLoS Comput Biol 12(4):e1004881

    Article  Google Scholar 

  88. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361

    Article  Google Scholar 

  89. Turing AM (1952) The chemical basis of morphogenesis. Philos Trans R Soc B 237(641):37–72

    MathSciNet  MATH  Google Scholar 

  90. Maini PK, Woolley TE, Baker RE, Gaffney EA, Lee SS (2012) Turing’s model for biological pattern formation and the robustness problem. Interface Focus 2(4):487–496

    Article  Google Scholar 

  91. Gomez MM, Arcak M (2017) A tug-of-war mechanism for pattern formation in a genetic network. ACS Synth Biol 6(11):2056–2066

    Article  Google Scholar 

  92. Macnamara CK, Chaplain MA (2016) Diffusion driven oscillations in gene regulatory networks. J Theor Biol 407:51–70

    Article  MathSciNet  Google Scholar 

  93. Koh W, Blackwell KT (2011) An accelerated algorithm for discrete stochastic simulation of reaction–diffusion systems using gradient-based diffusion and tau-leaping. J Chem Phys 134(15):04B612

    Article  Google Scholar 

  94. Kim C, Nonaka A, Bell JB, Garcia AL, Donev A (2017) Stochastic simulation of reaction–diffusion systems: a fluctuating-hydrodynamics approach. J Chem Phys 146(12):124110

    Article  Google Scholar 

  95. Gorochowski T (2016) Agent-based modelling in synthetic biology. Essays Biochem 60(4):325–336

    Article  Google Scholar 

  96. Sklar E (2007) NetLogo, a multi-agent simulation environment. Artif Life 13(3):303–311

    Article  Google Scholar 

  97. North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P (2013) Complex adaptive systems modeling with Repast Simphony. Complex Adapt Syst Model 1(1):3

    Article  Google Scholar 

  98. Gorochowski TE, Matyjaszkiewicz A, Todd T, Oak N, Kowalska K, Reid S, Tsaneva-Atanasova KT, Savery NJ, Grierson CS, di Bernardo M (2012) BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLoS One 7(8):e42790

    Article  Google Scholar 

  99. Matyjaszkiewicz A, Fiore G, Annunziata F, Grierson CS, Savery NJ, Marucci L, di Bernardo M (2017) BSim 2.0: an advanced agent-based cell simulator. ACS Synth Biol 6(10):1969–1972

    Article  Google Scholar 

  100. Jang SS, Oishi KT, Egbert RG, Klavins E (2012) Specification and simulation of synthetic multicelled behaviors. ACS Synth Biol 1(8):365–374

    Article  Google Scholar 

  101. Oishi K, Klavins E (2014) Framework for engineering finite state machines in gene regulatory networks. ACS Synth Biol 3(9):652–665

    Article  Google Scholar 

  102. Gutiérrez M, Gregorio-Godoy P, Perez del Pulgar G, Muñoz LE, Sáez S, Rodríguez-Patón A (2017) A new improved and extended version of the multicell bacterial simulator gro. ACS Synth Biol 6(8):1496–1508

    Article  Google Scholar 

  103. Khan A, Mandal S, Pal RK, Saha G (2016) Construction of gene regulatory networks using recurrent neural networks and swarm intelligence. Scientifica 2016:1060843

    Article  Google Scholar 

  104. Stražar M, Mraz M, Zimic N, Moškon M (2013) An adaptive genetic algorithm for parameter estimation of biological oscillator models to achieve target quantitative system response. Nat Comput 13(1):119–127

    Article  MathSciNet  Google Scholar 

  105. Jana B, Mitra S, Acharyya S (2019) Repository and mutation based particle swarm optimization (RPMSO): a new PSO variant applied to reconstruction of gene regulatory network. Appl Soft Comput 74:330–355

    Article  Google Scholar 

  106. Chen Y, Yan J, Feng J, Sareh P (2021) PSO-based metaheuristic design generation of non-trivial flat-foldable origami tessellations with degree-4 vertices. J Mech Des 143(1):011703

    Article  Google Scholar 

  107. Chen Y, Yan J, Sareh P, Feng J (2020) Feasible prestress modes for cable-strut structures with multiple self-stress states using particle swarm optimization. J Comput Civ Eng 34(3):04020003

    Article  Google Scholar 

  108. Hafner M, Koeppl H, Hasler M, Wagner A (2009) “Glocal” robustness analysis and model discrimination for circadian oscillators. PLoS Comput Biol 5(10):e1000534

  109. Pušnik Ž, Mraz M, Zimic N, Moškon M (2019) Computational analysis of viable parameter regions in models of synthetic biological systems. J Biol Eng 13(1):75

    Article  Google Scholar 

  110. Schillings C, Sunnåker M, Stelling J, Schwab C (2015) Efficient characterization of parametric uncertainty of complex (bio) chemical networks. PLoS Comput Biol 11(8):e1004457

    Article  Google Scholar 

  111. Ji W, Shi H, Zhang H, Sun R, Xi J, Wen D, Feng J, Chen Y, Qin X, Ma Y, Luo W, Deng L, Lin H, Yu R, Ouyang Q (2013) A formalized design process for bacterial consortia that perform logic computing. PLoS One 8(2):e57482

    Article  Google Scholar 

  112. Pedersen M, Phillips A (2009) Towards programming languages for genetic engineering of living cells. J R Soc Interface 6(Suppl 4):S437–S50

    Google Scholar 

  113. Macía J, Solé R (2014) How to make a synthetic multicellular computer. PLoS One 9(2):e81248

    Article  Google Scholar 

  114. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  115. de Lorenzo V (2011) Beware of metaphors: chasses and orthogonality in synthetic biology. Bioeng Bugs 2(1):3–7

    Article  MathSciNet  Google Scholar 

  116. Arkin AP (2013) A wise consistency: engineering biology for conformity, reliability, predictability. Curr Opin Chem Biol 17(6):893–901

    Article  Google Scholar 

  117. Decoene T, De Paepe B, Maertens J, Coussement P, Peters G, De Maeseneire SL, De Mey M (2018) Standardization in synthetic biology: an engineering discipline coming of age. Crit Rev Biotechnol 38(5):647–656

    Article  Google Scholar 

  118. Vilanova C, Porcar M (2014) iGEM 2.0—refoundations for engineering biology. Nat Biotechnol 32(5):420–424

    Article  Google Scholar 

  119. Galdzicki M, Clancy KP, Oberortner E, Pocock M, Quinn JY, Rodriguez CA, Roehner N, Wilson ML, Adam L, Anderson JC et al (2014) The synthetic biology open language (SBOL) provides a community standard for communicating designs in synthetic biology. Nat Biotechnol 32(6):545–550

    Article  Google Scholar 

  120. Beal J, Nguyen T, Gorochowski TE, Goñi-Moreno A, Scott-Brown J, McLaughlin JA, Madsen C, Aleritsch B, Bartley B, Bhakta S et al (2019) Communicating structure and function in synthetic biology diagrams. ACS Synth Biol 8(8):1818–1825

    Article  Google Scholar 

  121. Le Novere N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM et al (2009) The systems biology graphical notation. Nat Biotechnol 27(8):735–741

    Article  Google Scholar 

  122. Sarpeshkar R (2014) Analog synthetic biology. Philos Trans R Soc A 372(2012):20130110

    Article  Google Scholar 

  123. Song T, Garg S, Mokhtar R, Bui H, Reif J (2016) Analog computation by DNA strand displacement circuits. ACS Synth Biol 5(8):898–912

    Article  Google Scholar 

  124. Teo JJ, Woo SS, Sarpeshkar R (2015) Synthetic biology: a unifying view and review using analog circuits. IEEE Trans Biomed Circuits Syst 9(4):453–474

    Article  Google Scholar 

  125. Kendon V, Sebald A, Stepney S (2015) Heterotic computing: past, present and future. Philos Trans A Math Phys Eng Sci 373(2046):20140225

    Google Scholar 

  126. Goni-Moreno A, Nikel PI (2019) High-performance biocomputing in synthetic biology-integrated transcriptional and metabolic circuits. Front Bioeng Biotechnol 7:40

    Article  Google Scholar 

  127. Kaveh A (2013) Optimal analysis of structures by concepts of symmetry and regularity. Springer, Berlin

    Book  MATH  Google Scholar 

  128. Pandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, Faulon JL (2019) Metabolic perceptrons for neural computing in biological systems. Nat Commun 10(1):1–13

    Article  Google Scholar 

  129. Huang S (2019) Towards multicellular biological deep neural nets based on transcriptional regulation. Arxiv

  130. Sarkar K, Bonnerjee D, Bagh S (2020) A single layer artificial neural network with engineered bacteria. Arxiv

  131. Li X, Rizik L, Daniel R (2020) Synthetic neural-like computing in microbial consortia for pattern recognition. Biorxiv

  132. Karig D, Martini KM, Lu T, DeLateur NA, Goldenfeld N, Weiss R (2018) Stochastic Turing patterns in a synthetic bacterial population. Proc Natl Acad Sci USA 115(26):6572–6577

    Article  Google Scholar 

  133. Sekine R, Shibata T, Ebisuya M (2018) Synthetic mammalian pattern formation driven by differential diffusivity of nodal and lefty. Nat Commun 9(1):1–11

    Article  Google Scholar 

  134. Nebreda SD, Pla J, Rocamora BV, Pinero J, Conde N, Sole R (2020) Synthetic Turing patterns in engineered microbial consortia. Biorxiv

  135. Kondo S, Miura T (2010) Reaction–diffusion model as a framework for understanding biological pattern formation. Science 329(5999):1616–1620

    Article  MathSciNet  MATH  Google Scholar 

  136. Kisel’ák J, Lu Y, Švihra J, Szépe P, Stehlík M (2020) “SPOCU”: scaled polynomial constant unit activation function. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05182-1

    Article  Google Scholar 

  137. Buscarino A, Corradino C, Fortuna L, Frasca M, Chua LO (2016) Turing patterns in memristive cellular nonlinear networks. IEEE Trans Circuits Syst I Regul Pap 63(8):1222–1230

    Article  MathSciNet  MATH  Google Scholar 

  138. Bucolo M, Buscarino A, Corradino C, Fortuna L, Frasca M (2019) Turing patterns in the simplest MCNN. Nonlinear Theory Appl 10(4):390–398

    Google Scholar 

  139. Andras P (2002) Computation with chaotic patterns. Neurocomputing 44:263–268

    Article  MATH  Google Scholar 

  140. Beal J, Weiss R, Densmore D, Adler A, Appleton E, Babb J, Bhatia S, Davidsohn N, Haddock T, Loyall J, Schantz R, Vasilev V (2012) An end-to-end work flow for engineering of biological networks from high-level specifications. ACS Synth Biol 1(8):317–331

    Article  Google Scholar 

  141. Lissek T (2017) Interfacing neural network components and nucleic acids. Front Bioeng Biotechnol 5:53

    Article  Google Scholar 

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Funding

The research was partially supported by the scientific research programme Pervasive Computing (P2-0359) financed by the Slovenian Research Agency in the years from 2013 to 2023 and by the basic research project CholesteROR in metabolic liver diseases (J1-9176) financed by the Slovenian Research Agency in the years from 2018 to 2021. The funding sources did not have any role in the study design, in the collection, analysis, and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication.

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  1. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Miha Moškon, Roman Komac, Nikolaj Zimic & Miha Mraz

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  1. Miha Moškon
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  2. Roman Komac
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  3. Nikolaj Zimic
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Contributions

M.M. reviewed the literature, outlined the processor topology, and wrote the manuscript. R.K. implemented the processor model and conducted its optimisation and analysis. M.M. and N.Z. provided critical feedback and helped shape the research, analysis and manuscript. All authors read and approved the final manuscript.

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Correspondence to Miha Moškon.

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Moškon, M., Komac, R., Zimic, N. et al. Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Comput & Applic 33, 8923–8938 (2021). https://doi.org/10.1007/s00521-021-05711-6

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  • DOI: https://doi.org/10.1007/s00521-021-05711-6

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